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Pandas join issue: columns overlap but no suffix specified
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How to Fix: columns overlap but no suffix specified
This error occurs when we join two pandas data frames that have one or more same columns but there is no suffix specified to differentiate them.
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Solve “columns overlap but no suffix specified” in Pandas
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Pandas join issue: columns overlap but no suffix specified
This error indicates that the two tables have one or more column names that have the same column name.
The error message translates to: “I can see the same column in both tables but you haven’t told me to rename either one before bringing them into the same table”
You either want to delete one of the columns before bringing it in from the other on using del df[‘column name’] , or use lsuffix to re-write the original column, or rsuffix to rename the one that is being brought in.
How to Fix: columns overlap but no suffix specified
One error you may encounter when using pandas is:
ValueError : columns overlap but no suffix specified: Index([‘column’], dtype=’object’)
This error occurs when you attempt to join together two data frames that share at least one common column name and a suffix is not provided for either the left or right data frame to distinguish between the columns in the new data frame.
There are two ways to fix this error:
Solution 1: Provide suffix names.
df1. join (df2, how = ‘ left ‘, lsuffix=’ left ‘, rsuffix=’ right ‘)
Solution 2: Use the merge function instead.
df1. merge (df2, how = ‘ left ‘)
The following example shows how to fix this error in practice.
How to Reproduce the Error
Suppose we attempt to join together the following two data frames:
import pandas as pd #create first data frame df1 = pd. DataFrame ({‘ player ‘: [‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’], ‘ points ‘: [5, 7, 7, 9, 12, 9], ‘ assists ‘: [11, 8, 10, 6, 6, 5]}) #create second data frame df2 = pd. DataFrame ({‘ player ‘: [‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’], ‘ rebounds ‘: [4, 4, 6, 9, 13, 16], ‘ steals ‘: [2, 2, 1, 4, 3, 2]}) #attempt to perform left join on data frames df1. join (df2, how = ‘ left ‘) ValueError : columns overlap but no suffix specified: Index([‘player’], dtype=’object’)
We receive an error because the two data frames both share the “player” column, but there is no suffix provided for either the left or right data frame to distinguish between the columns in the new data frame.
How to Fix the Error
One way to fix this error is to provide a suffix name for either the left or right data frame:
#perform left join on data frames with suffix provided df1. join (df2, how = ‘ left ‘, lsuffix=’ left ‘, rsuffix=’ right ‘) playerleft points assists playerright rebounds steals 0 A 5 11 A 4 2 1 B 7 8 B 4 2 2 C 7 10 C 6 1 3 D 9 6 D 9 4 4 E 12 6 E 13 3 5 F 9 5 F 16 2
Another way to fix this error is to simply use the merge() function, which doesn’t encounter this problem when joining two data frames together:
#merge two data frames df1. merge (df2, how = ‘ left ‘) player points assists rebounds steals 0 A 5 11 4 2 1 B 7 8 4 2 2 C 7 10 6 1 3 D 9 6 9 4 4 E 12 6 13 3 5 F 9 5 16 2
Notice that the merge() function simply drops any names from the second data frame that already belong to the first data frame.
Additional Resources
How to Merge Two Pandas DataFrames on Index
How to Merge Pandas DataFrames on Multiple Columns
How to Add a Numpy Array to a Pandas DataFrame
How to Fix: columns overlap but no suffix specified
How to Fix: columns overlap but no suffix specified
In this article, we will fix the error: columns overlap but no suffix specified in Python.
This error occurs when we join two pandas data frames that have one or more same columns but there is no suffix specified to differentiate them.
Error:
ValueError: columns overlap but no suffix specified
Case of this error occurrence by an example:
Python3
import pandas as pd import numpy as np sepal_length = [ 5.1 , 4.9 , 4.7 , 4.6 , 5.0 , 5.4 , 4.2 , 5.3 , 4.4 , 4.8 ] petal_length = [ 3.3 , 4.6 , 4.7 , 5.6 , 6.7 , 5.0 , 4.8 , 4.1 , 3.6 , 4.4 ] petal_width = [ 3.6 , 5.6 , 5.4 , 4.6 , 4.4 , 5.0 , 4.9 , 5.6 , 5.2 , 4.4 ] df1 = pd.DataFrame({ ‘sepal_length(cm)’ : sepal_length, ‘petal_length(cm)’ : petal_length}) df2 = pd.DataFrame({ ‘sepal_length(cm)’ : sepal_length, ‘petal_width(cm)’ : petal_width}) print (df1.join(df2))
Output:
ValueError: columns overlap but no suffix specified: Index([‘sepal_length(cm)’], dtype=’object’)
Reason for the error :
The data frames df1 and df2 have the common column sepal_length(cm) that is the intersection of two data frames is not empty. To check if there are any intersection columns :
print(df1.columns.intersection(df2.columns))
Output:
Index([‘sepal_length(cm)’], dtype=’object’)
sepal_length(cm) is the intersection column here.
Fixing the error:
This error can be fixed by using the join() or merge() method on the two data frames.
Method1 : Using join() method
The suffixes should be given while using the join method to avoid this error. By default, it does inner join on two tables. This can be changed by mentioning the parameters in join() function.
Syntax:
df1.join(df2, lsuffix=’suffix_name’, rsuffix=’suffix_name’)
where
df1 is the first dataframe
df2 is the second dataframe
Example:
Python3
import pandas as pd import numpy as np sepal_length = [ 5.1 , 4.9 , 4.7 , 4.6 , 5.0 , 5.4 , 4.2 , 5.3 , 4.4 , 4.8 ] petal_length = [ 3.3 , 4.6 , 4.7 , 5.6 , 6.7 , 5.0 , 4.8 , 4.1 , 3.6 , 4.4 ] petal_width = [ 3.6 , 5.6 , 5.4 , 4.6 , 4.4 , 5.0 , 4.9 , 5.6 , 5.2 , 4.4 ] df1 = pd.DataFrame({ ‘sepal_length(cm)’ : sepal_length, ‘petal_length(cm)’ : petal_length}) df2 = pd.DataFrame({ ‘sepal_length(cm)’ : sepal_length, ‘petal_width(cm)’ : petal_width}) print (df1.join(df2, lsuffix = ‘_left’ , rsuffix = ‘_right’ ))
Output:
Method 2: Using merge() method
This merge() method considers the common column in the 2 data frames and drops the common column in one of the data frames.
Syntax:
pd.merge(df1,df2, how=’join_type’, on=None, left_on= None, right_on = None, left_index =boolean, right_index=boolean, sort=boolean)
where,
df1 is the first dataframe
df2 is the second dataframe
The type of joining and the names of left and right data frames common columns can also be mentioned as parameters in the merge() function.
Example:
Python3
import pandas as pd import numpy as np sepal_length = [ 5.1 , 4.9 , 4.7 , 4.6 , 5.0 , 5.4 , 4.2 , 5.3 , 4.4 , 4.8 ] petal_length = [ 3.3 , 4.6 , 4.7 , 5.6 , 6.7 , 5.0 , 4.8 , 4.1 , 3.6 , 4.4 ] petal_width = [ 3.6 , 5.6 , 5.4 , 4.6 , 4.4 , 5.0 , 4.9 , 5.6 , 5.2 , 4.4 ] df1 = pd.DataFrame({ ‘sepal_length(cm)’ : sepal_length, ‘petal_length(cm)’ : petal_length}) df2 = pd.DataFrame({ ‘sepal_length(cm)’ : sepal_length, ‘petal_width(cm)’ : petal_width}) print (pd.merge(df1, df2))
Output:
Solve “columns overlap but no suffix specified” in Pandas — Roel Peters
Surprisingly, the Pandas error “columns overlap but no suffix specified”, is one I ran into surprisingly late. Solving it is usually very straightforward. We’ll tackle it in this blog post.
First, let’s take a closer look at the error:
“ValueError: columns overlap but no suffix specified: Index([
], dtype=’object’)”
When you run into this error, in all likelihood you are using the Pandas join method. What the error is telling you is that in the DataFrames you’re trying to join, there are some column names that exist in both DataFrames. Pandas is telling you to provide a suffix for the column names in both DataFrames, so you will be able to distinguish the difference in the joined DataFrame.
In the following code snippet, you can see that the columns ‘b’ and ‘c’ exist in both DataFrames and we don’t join on it. This will produce the error.
df1 = pd.DataFrame({‘a’: [1,0,2,3,4],’b’: [0,0,0,0,0], ‘c’: [9,12,15,16,54]}) df1.set_index(‘a’) df2 = pd.DataFrame({‘a’: [1,0,2,3,4],’b’: [5,0,3,1,0], ‘c’: [8,2,100,26,23]}) df2.set_index(‘a’) df1.join(df2, how = ‘left’)
Solving the error is easy. Simply provide a suffix.
df1.join(df2, how = ‘left’, lsuffix = ‘_left’, rsuffix = ‘_right’)
There is an alternative solution. Use the merge method. It will give priority to the columns of the DataFrame that you provided in the ‘how’ argument.
df1.merge(df2, how = ‘left’)
It depends on what you expect from joining your DataFrames but there are use cases for both solutions.
A final note: I ran into this error in a very unusual situation: both DataFrames initially had no overlapping columns. However, I joined the DataFrame twices and assigned it to one of the initial DataFrames.
Pandas join issue: columns overlap but no suffix specified
Question :
Pandas join issue: columns overlap but no suffix specified I have following 2 data frames: df_a = mukey DI PI 0 100000 35 14 1 1000005 44 14 2 1000006 44 14 3 1000007 43 13 4 1000008 43 13 df_b = mukey niccdcd 0 190236 4 1 190237 6 2 190238 7 3 190239 4 4 190240 7 When I try to join these 2 dataframes: join_df = df_a.join(df_b,on= ‘mukey’ ,how= ‘left’ ) I get the error: *** ValueError: columns overlap but no suffix specified: Index([ u’mukey’ ], dtype= ‘object’ ) Why is this so? The dataframes do have common ‘mukey’ values. || Source Asked By: user308827
Answer #1: Your error on the snippet of data you posted is a little cryptic, in that because there are no common values, the join operation fails because the values don’t overlap it requires you to supply a suffix for the left and right hand side: In [ 173 ]: df_a.join(df_b, on= ‘mukey’ , how= ‘left’ , lsuffix= ‘_left’ , rsuffix= ‘_right’ ) Out[ 173 ]: mukey_left DI PI mukey_right niccdcd index 0 100000 35 14 NaN NaN 1 1000005 44 14 NaN NaN 2 1000006 44 14 NaN NaN 3 1000007 43 13 NaN NaN 4 1000008 43 13 NaN NaN merge works because it doesn’t have this restriction: In [ 176 ]: df_a.merge(df_b, on= ‘mukey’ , how= ‘left’ ) Out[ 176 ]: mukey DI PI niccdcd 0 100000 35 14 NaN 1 1000005 44 14 NaN 2 1000006 44 14 NaN 3 1000007 43 13 NaN 4 1000008 43 13 NaN Answered By: EdChum
Answer #2: The .join() function is using the index of the passed as argument dataset, so you should use set_index or use .merge function instead. Please find the two examples that should work in your case: join_df = LS_sgo.join(MSU_pi.set_index( ‘mukey’ ), on= ‘mukey’ , how= ‘left’ ) or join_df = df_a.merge(df_b, on= ‘mukey’ , how= ‘left’ ) Answered By: Velizar VESSELINOV
Answer #3: This error indicates that the two tables have the 1 or more column names that have the same column name. The error message translates to: “I can see the same column in both tables but you haven’t told me to rename either before bringing one of them in” You either want to delete one of the columns before bringing it in from the other on using del df[‘column name’], or use lsuffix to re-write the original column, or rsuffix to rename the one that is being brought it. df_a.join(df_b, on= ‘mukey’ , how= ‘left’ , lsuffix= ‘_left’ , rsuffix= ‘_right’ ) Answered By: user1761806
Answer #4: Mainly join is used exclusively to join based on the index,not on the attribute names,so change the attributes names in two different dataframes,then try to join,they will be joined,else this error is raised Answered By: user12690524
Answer #5: The error indicates that the two tables have the 1 or more column names that have the same column name. Anyone with the same error who doesn’t want to provide a suffix can rename the columns instead. Also make sure the index of both DataFrames match in type and value if you don’t want to provide the on=’mukey’ setting. df_a = df_a.rename(columns={ ‘a_old’ : ‘a_new’ , ‘a2_old’ : ‘a2_new’ }) df_a = df_a.set_index([ ‘mukus’ ]) df_b = df_b.set_index([ ‘mukus’ ]) df_a.join(df_b) Answered By: Joe Eifert
How to Solve Python ValueError: Columns overlap but no suffix specified
If you try to join together two DataFrames that share one or more column names but do not provide a suffix for either the right or left DataFrame to differentiate the between the columns, you will raise the ValueError: Columns overlap but no suffix specified.
To solve this error, you can use the merge function. For example df1.merge(df2, how = ‘left’) . Or you can use the join() method and provide a suffix for the left and right DataFrames, for example,
df1.merge(df2, how=’left’, lsuffix=’left’, rsuffix=’right’)
This tutorial will go through the error in detail and how to solve it with code examples.
ValueError: Columns overlap but no suffix specified
In Python, a value is a piece of information stored within a particular object. We will encounter a ValueError in Python when using a built-in operation or function that receives an argument that is the right type but an inappropriate value. In this specific error, the data we use during a join operation is the correct type, DataFrame, but the DataFrames have one or more identical columns without a distinguishing suffix specified.
Example
Let’s look at an example of two DataFrames containing information about particles that we want to join together. The first DataFrame contains the particle names and the masses of each particle. The second DataFrame contains the particle names, the charge, and the spin of each particle. Let’s look at the code:
import pandas as pd df1 = pd.DataFrame({‘particle_name’:[‘electron’, ‘muon’, ‘tau’, ‘W-boson’, ‘proton’, ‘Higgs boson’], ‘mass (MeV)’:[0.51, 106.7, 1776.9, 80433.5, 938.3, 125100]}) df2 = pd.DataFrame({‘particle_name’:[‘electron’, ‘muon’, ‘tau’, ‘W-boson’, ‘proton’, ‘Higgs boson’], ‘charge’:[-1, -1, -1, -1, 1, 0], ‘spin’:[0.5, 0.5, 0.5, 1, 0.5, 0]}) print(df1) print(df2)
Let’s run the first part of the program to see the DataFrames:
particle_name mass (MeV) 0 electron 0.51 1 muon 106.70 2 tau 1776.90 3 W-boson 80433.50 4 proton 938.30 5 Higgs boson 125100.0 particle_name charge spin 0 electron -1 0.5 1 muon -1 0.5 2 tau -1 0.5 3 W-boson -1 1.0 4 proton 1 0.5 5 Higgs boson 0 0.0
Next, we will try to join the DataFrames using the join() method:
df3= df1.join(df2, how=’left’)
Let’s run the code to see what happens:
ValueError: columns overlap but no suffix specified: Index([‘particle_name’], dtype=’object’)
The error occurs because df1 and df2 share the particle_name column, and there is no suffix to differentiate between the columns in the new DataFrame df3 .
Solution #1: Use merge()
We can solve this error by using the merge() function. Let’s look at the revised code:
df3 = df1.merge(df2) print(df3)
Let’s run the code to see the result:
particle_name mass (MeV) charge spin 0 electron 0.51 -1 0.5 1 muon 106.70 -1 0.5 2 tau 1776.90 -1 0.5 3 W-boson 80433.50 -1 1.0 4 proton 938.30 1 0.5 5 Higgs boson 125100.00 0 0.0
We successfully merged the DataFrames. The merge() method drops any value in the common column particle_name for the right DataFrame that already exists in the left DataFrame.
Solution #2: Use join() with lsuffix and rsuffix
We can use the join() method and provide a suffix name for the left and/or right DataFrames. The parameters to set are suffix is lsuffix for the left DataFrame and rsuffix for the right DataFrame. Let’s look at the revised code:
df3 = df1.join(df2, how=’left’, rsuffix=’_2′) print(df3)
In the above code, we set the suffix for the right DataFrame to _2 . Let’s run the code to get the result:
particle_name mass (MeV) particle_name_2 charge spin 0 electron 0.51 electron -1 0.5 1 muon 106.70 muon -1 0.5 2 tau 1776.90 tau -1 0.5 3 W-boson 80433.50 W-boson -1 1.0 4 proton 938.30 proton 1 0.5 5 Higgs boson 125100.00 Higgs boson 0 0.0
We successfully joined the DataFrames using _2 as a suffix for particle_name in the right DataFrame.
Summary
Congratulations on reading to the end of the tutorial! The ValueError: Columns overlap but no suffix provided occurs when you try to join two DataFrames that share one or more columns with no distinguishing suffix provided. To solve this error you can use the merge() method or the join() method with the lsuffix and/or rsuffix parameter values specified.
For further reading on errors involving Pandas, go to the article:
How to Solve Python ValueError: Trailing data
For further reading on Pandas, go to the article: Introduction to Pandas: A Complete Tutorial for Beginners.
Have fun and happy researching!
columns overlap but no suffix specified: (to_rename)
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Pandas join issue: columns overlap but no suffix specified
Because the join() method is in the string class, instead of the list class?
I agree it looks funny.
See http://www.faqs.org/docs/diveintopython/odbchelper_join.html:
Historical note. When I first learned Python, I expected join to be a method of a list, which would take the delimiter as an argument. Lots of people feel the same way, and there’s a story behind the join method. Prior to Python 1.6, strings didn’t have all these useful methods. There was a separate string module which contained all the string functions; each function took a string as its first argument. The functions were deemed important enough to put onto the strings themselves, which made sense for functions like lower, upper, and split. But many hard-core Python programmers objected to the new join method, arguing that it should be a method of the list instead, or that it shouldn’t move at all but simply stay a part of the old string module (which still has lots of useful stuff in it). I use the new join method exclusively, but you will see code written either way, and if it really bothers you, you can use the old string.join function instead.
— Mark Pilgrim, Dive into Python
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